How much can an AI remember?
The context window, forgetting, the cost of long inputs, and workarounds.
Transcript
A language model can only read a fixed number of tokens at once. That is its context window.
Your prompt, the chat history, and even its own reply all have to fit inside it.
When a conversation grows longer, the oldest tokens fall out of the window.
The model has no memory outside it, so it can forget what you said earlier.
Attention compares every token with every other, so longer windows cost far more compute.
Tricks like summarising the history, or retrieving only what matters, keep the important parts in view.
The context window is the model's working memory. Choose carefully what goes in.
More in this series
1:14How does AI work?
Neurons and weights, learning from mistakes, and predicting the next word.
1:13How does AI read a sentence?
Tokens, embeddings and attention, stacked in layers to score the next token.
1:17What are billions of parameters?
What a parameter is, how big a billion is, and why big models need racks of GPUs.
1:26How do networks learn from errors?
Loss as a landscape, gradient descent and backpropagation.
1:24How is a chatbot trained?
Pretraining, fine-tuning and human feedback turn a text predictor into an assistant.
1:23What does temperature do?
Scores become probabilities, and temperature sharpens or flattens them.
1:19How does AI draw pictures?
Diffusion models add noise to learn, then remove it to create, steered by a prompt.
1:15How can AI use your own documents?
Retrieval-augmented generation: embed, retrieve, augment the prompt, generate.
1:14Why does AI make things up?
Likely is not the same as true: gaps, snowballing errors, and what helps.
1:09What is an AI agent?
A model in a loop with tools and guardrails.
1:12Can AI be biased?
Skewed data, where bias comes from, proxies, and how to audit and fix it.
1:10What is overfitting?
Underfit, good fit and overfit curves, train vs test error, and the fixes.